Again not the easiest paper in the list, again mostly algorithmic and requiring some background on how it impacted the field. Even though Agnė also went through the Elements of Statistical Learning by Hastie, Friedman and Tibshirani, it was hard to get away from the paper to analyse more widely the importance of the paper, the connection with the Bayesian (linear) literature of the 70’s, its algorithmic and inferential aspects, like the computational cost, and the recent extensions like Bayesian LASSO. Or the issue of handling n<p models. Remember that one of the S in LASSO stands for shrinkage: it was quite pleasant to hear again about ridge estimators and Stein’s unbiased estimator of the risk, as those were themes of my Ph.D. thesis… (I hope the students do not get discouraged by the complexity of those papers: there were fewer questions and fewer students this time. Next week, the compass will move to the Bayesian pole with a talk on Lindley and Smith’s 1973 linear Bayes paper by one of my PhD students.)

The conference in honour of Larry Brown was quite exciting, with lots of old friends gathered in Philadelphia and lots of great talks either recollecting major works of Larry and coauthors or presenting fairly interesting new works. Unsurprisingly, a large chunk of the talks was about admissibility and minimaxity, with John Hartigan starting the day re-reading Larry masterpiece 1971 paper linking admissibility and recurrence of associated processes, a paper I always had trouble studying because of both its depth and its breadth! Bill Strawderman presented a new if classical minimaxity result on matrix estimation and Anirban DasGupta some large dimension consistency results where the choice of the distance (total variation versus Kullback deviance) was irrelevant. Ed George and Susie Bayarri both presented their recent work on g-priors and their generalisation, which directly relate to our recent paper on that topic. On the afternoon, Holger Dette showed some impressive mathematics based on Elfving’s representation and used in building optimal designs. I particularly appreciated the results of a joint work with Larry presented by Robert Wolpert where they classified all Markov stationary infinitely divisible time-reversible integer-valued processes. It produced a surprisingly small list of four cases, two being trivial.. The final talk of the day was about homology, which sounded a priori rebutting, but Robert Adler made it extremely entertaining, so much that I even failed to resent the powerpoint tricks! The next morning, Mark Low gave a very emotional but also quite illuminating about the first results he got during his PhD thesis at Cornell (completing the thesis when I was using Larry’s office!). Brenda McGibbon went back to the three truncated Poisson papers she wrote with Ian Johnstone (via gruesome 13 hour bus rides from Montréal to Ithaca!) and produced an illuminating explanation of the maths at work for moving from the Gaussian to the Poisson case in a most pedagogical and enjoyable fashion. Larry Wasserman explained the concepts at work behind the lasso for graphs, entertaining us with witty acronyms on the side!, and leaving out about 3/4 of his slides! (The research group involved in this project produced an R package called huge.) Joe Eaton ended up the morning with a very interesting result showing that using the right Haar measure as a prior leads to a matching prior, then showing why the consequences of the result are limited by invariance itself. Unfortunately, it was then time for me to leave and I will miss (in both meanings of the term) the other half of the talks. Especially missing Steve Fienberg’s talk for the third time in three weeks! Again, what I appreciated most during those two days (besides the fact that we were all reunited on the very day of Larry’s birthday!) was the pain most speakers went to to expose older results in a most synthetic and intuitive manner… I also got new ideas about generalising our parallel computing paper for random walk Metropolis-Hastings algorithms and for optimising across permutation transforms.

After a huge delay, since the project started in 2006 and was first presented in Banff in 2007 (as well as included in the Bayesian Core), Gilles Celeux, Mohammed El Anbari, Jean-Michel Marin, and myself have eventually completed our paper on using hyper-g priors variable selection and regularisation in linear models . The redaction of this paper was mostly delayed due to the publication of the 2007 JASA paper by Feng Liang, Rui Paulo, German Molina, Jim Berger, and Merlise Clyde, Mixtures of g-priors for Bayesian variable selection. We had indeed (independently) obtained very similar derivations based on hypergeometric function representations but, once the above paper was published, we needed to add material to our derivation and chose to run a comparison study between Bayesian and non-Bayesian methods for a series of simulated and true examples. It took a while to Mohammed El Anbari to complete this simulation study and even longer for the four of us to convene and agree on the presentation of the paper. The only difference between Liang et al.’s (2007) modelling and ours is that we do not distinguish between the intercept and the other regression coefficients in the linear model. On the one hand, this gives us one degree of freedom that allows us to pick an improper prior on the variance parameter. On the other hand, our posterior distribution is not invariant under location transforms, which was a point we heavily debated in Banff… The simulation part shows that all “standard” Bayesian solutions lead to very similar decisions and that they are much more parsimonious than regularisation techniques.

Two other papers posted on arXiv today address the model choice issue. The first one by Bruce Lindsay and Jiawei Liu introduces a credibility index, and the second one by Bazerque, Mateos, and Giannakis considers group-lasso on splines for spectrum cartography.

For the final day of the meeting, after a good one hour run to the end of the Benidorm bay (for me at least!), we got treated to great talks, culminating with the fitting conclusion given by the conference originator, José Bernardo. The first talk of the day was Guido Consonni’s, who introduced a new class of non-local priors to deal with variable selection. From my understanding, those priors avoid a neighbourhood of zero by placing a polynomial prior on the regression coefficients in order to discriminate better between the null and the alternative,

but the influence of the power h seems to be drastic, judging from the example showed by Guido where a move from h=0 to h=1, modified the posterior probability from 0.091 to 0.99 for the same dataset. The discussion by Jim Smith was a perfect finale to the Valencia meetings, Jim being much more abrasive than the usual discussant (while always giving the impression of being near a heart attack//!) The talk from Sylvia Früwirth-Schnatter purposely borrowed Nick Polson’ s title Shrink globally, act locally, and was also dealing with the Bayesian (re)interpretation of Lasso. (I was again left with the impression of hyperparameters that needed to be calibrated but this impression may change after I read the paper!) The talk by Xiao-Li Meng was as efficient as ever with Xiao-Li! Despite the penalising fact of being based on a discussion he wrote for Statistical Science, he managed to convey a global and convincing picture of likelihood inference in latent variable models, while having the audience laugh most of the talk, a feat repeated by his discussant, Ed George. The basic issue of treating latent variables as parameters offers no particular difficulty in Bayesian inference but this is not true for likelihood models, as shown by both Xiao-Li and Ed. The last talk of the València series managed to make a unifying theory out of the major achievements of José Bernardo and, while I have some criticisms about the outcome, this journey back to decision theory, intrinsic losses and reference priors was nonetheless a very appropriate supplementary contribution of José to this wonderful series of meetings…. Luis Perricchi discussed the paper in a very opinionated manner, defending the role of the Bayes factor, and the debate could have gone forever…Hopefully, I will find time to post my comments on José’s paper.

I am quite sorry I had to leave before the Savage prize session where the four finalists to the prize gave a lecture. Those finalists are of the highest quality as the prize is not given in years when the quality of the theses is not deemed high enough. I will also miss the final evening during which the DeGroot Prize is attributed. (When I received the prize for Bayesian Core. in 2004, I had also left in the morning Valparaiso, just before the banquet!)

Yesterday I talked about the paper of Nott and Cheng at the Bayesian model choice group and [with the help of the group] realised that my earlier comment on the paper

There is however one point with which I disagree, namely that the predictive on the submodel is obtained in the current paper by projecting a Monte Carlo or an MCMC sample from the predictive on the full model, while I think this is incorrect because the likelihood is then computed using the parameter for the full model. Using a projection of such a sample means at least reweighting by the ratio of the likelihoods…

was not completely accurate. The point is [I think] correct when considering the posterior distribution of the projected parameters. Thus, using a projection of an MCMC sample corresponing to the full model will not result in a sample from the posterior distribution of the projected parameters. On the other hand, projecting the MCMC sample in order to get the Kullback-Leibler distance posterior distribution as done in the applications of Section 7 of the paper is completely kosher, since this is a quantity that only depends on the full model parameters. Since Nott and Cheng do not consider the projected model at any time (even though Section 3 is slightly unclear, using a posterior on the projected parameter), there is nothing wrong in their paper and I do find quite interesting the idea that the lasso penalty allows for a simultaneous exploration of the most likely submodels without a recourse to a more advanced technique like reversible jump. (The comparison is obviously biased as the method does not provide a true posterior on the most likely submodels, only an approximation of their probability. Simulating from the constrained projected posterior would require extra steps.)